Transitional intermittency in boundary layers subjected to pressure gradient

Results are reported from an extensive series of experiments on boundary layers in which the location of pressure gradient and transition onset could be varied almost independently, by judicious use of tunnel wall liners and transition-fixing devices. The experiments show that the transition zone is sensitive to the pressure gradient especially near onset, and can be significantly asymmetric; no universal similarity appears valid in general. Observed intermittency distributions cannot be explained on the basis of the hypothesis, often made, that the spot propagates at speeds proportional to the local free-stream velocity but is otherwise unaffected by the pressure gradient

Comparing optimization strategies for force field parameterization

Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability of MD relies on the force field (FF) used to describe interatomic interactions. FF parameters are typically determined to reproduce selected material properties computed from density functional theory (DFT) and/or measured experimentally. A common practice in parameterizing FFs is to use least-squares local minimization algorithms. Genetic algorithms (GAs) have also been demonstrated as a viable global optimization approach, even for complex FFs. However, an understanding of the relative effectiveness and efficiency of different optimization techniques for the determination of FF parameters is still lacking. In this work, we evaluate various FF parameter optimization schemes, using as example a training data set calculated from DFT for different polymorphs of Ir$O_2$. The Morse functional form is chosen for the pairwise interactions and the optimization of the parameters against the training data is carried out using (1) multi-start local optimization algorithms: Simplex, Levenberg-Marquardt, and POUNDERS, (2) single-objective GA, and (3) multi-objective GA. Using random search as a baseline, we compare the algorithms in terms of reaching the lowest error, and number of function evaluations. We also compare the effectiveness of different approaches for FF parameterization using a test data set with known ground truth (i.e generated from a specific Morse FF). We find that the performance of optimization approaches differs when using the Test data vs. the DFT data. Overall, this study provides insight for selecting a suitable optimization method for FF parameterization, which in turn can enable more accurate prediction of material properties and chemical phenomena

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: A key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.United States. Army Research Office (Grant W911NF-16-1-0289)United States. Air Force Office of Scientific Research (Grant FA9550-16-1-0159)United States. Army Research Office (Grant W911NF-16-1-0042

WIATEC: A World Integrated Assessment Model of Global Trade Environment and Climate Change

This paper describes the structure of the World Integrated Assessment model of global Trade, Environmental, and Climate change (WIATEC).The model consists of a multi-regional multi-sectoral core CGE model linked to a climate model. The core CGE is based on an existing global trade and environment model called GTAP-E (Truong, 1999; Burniaux and Truong, 2002). A suite of different and interchangeable 'modules' are then built around this 'core' to enable the model to be able to handle a range of different policy issues such as CO2 emissions, abatement, trading, non-CO2 (CH4 and N2O) emissions, land use land use change and forestry (LULUCF) activities, and changing technologies in the electricity generation sector. The approach which uses a core model structure with different additional modules built around this core structure allows the overall model to be flexible and can be adapted to a range of different policy issues. We illustrate the usefulness of this approach in a policy experiment which looks at the interaction between emissions trading scheme and the promotion of renewable energy targets in the European Union climate policy